Abstract

Background: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. Method: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. Results: ALGO and TAU patients showed significant initial decreases in symptoms (p = .03 and p < .001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. Conclusion: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

title = "Texas Medication Algorithm Project, phase 3 (TMAP-3): Clinical results for patients with a history of mania",

abstract = "Background: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. Method: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. Results: ALGO and TAU patients showed significant initial decreases in symptoms (p = .03 and p < .001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. Conclusion: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.",

N2 - Background: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. Method: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. Results: ALGO and TAU patients showed significant initial decreases in symptoms (p = .03 and p < .001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. Conclusion: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

AB - Background: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. Method: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. Results: ALGO and TAU patients showed significant initial decreases in symptoms (p = .03 and p < .001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. Conclusion: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.